Quantum pattern recognition on real quantum processing units

Year: 2023

Authors: Das S., Zhang JF., Martina S., Suter D., Caruso F.

Autors Affiliation: Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, Italy; Univ Florence, European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; Tech Univ Dortmund, Fak Phys, D-44221 Dortmund, Germany; QSTAR, Largo Enr Fermi 2, I-50125 Florence, Italy; CNR INO, Largo Enr Fermi 2, I-50125 Florence, Italy.

Abstract: One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits, the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when run on real IBMQ processors. As test images, we use binary images with simple patterns, grayscale MNIST numbers and fashion MNIST images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy (NVs) center in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

Journal/Review: QUANTUM MACHINE INTELLIGENCE

Volume: 5 (1)      Pages from: 16-1  to: 16-17

KeyWords: Quantum computation; Quantum pattern recognition; Quantum image processing; Machine learning; Artificial intelligence; Quantum associative memory; NISQ
DOI: 10.1007/s42484-022-00093-x

ImpactFactor: 4.100
Citations: 4
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-24
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